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p-Index From 2021 - 2026
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ricardo Yauri
Universidad Tecnológica del Perú
Computer Science & IT Electrical & Electronics Engineering Engineering

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Embedded electronic system for evaluation of photovoltaic modules based on a current-voltage curve tracer Ricardo Yauri; Rafael Espino
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1281-1289

Abstract

The rapid growth of the market for the use of renewable energy has increased the use of solar energy which has a significant role in power generation. This requires the insertion of equipment capable of providing precise measurements of the photovoltaic modules, either to verify the operation of the installation or to find specific problems. In this scenario, the current versus voltage curve tracer is used to describe the electrical behavior of the photovoltaic system through all the operating possibilities, but it has an excessive cost for small installations. This paper presents the development of a current-voltage curve tracer, capable of performing current, voltage and power measurements, contributing to the creation of equipment to test photovoltaic installations. The methods to obtain the I-V curves are presented and the characteristics of the embedded electronic system, which is based on an electronic load, are defined. As results, the simulations carried out for the variable load control, acquisition circuits and the implemented system are shown. In addition, the operation of the human-machine interface and the comparison with a commercial equipment are shown for reference.
Recognition system based on artificial vision using OpenCV for discarding and detecting ceramics with defects Fernando Alvarado; Ricardo Yauri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1166-1173

Abstract

Early detection of defects through preventive maintenance is important in industry to avoid economic losses, as in the case of ceramic tile manufacturing, where manual inspection allows defective parts to advance in production, causing delays. The research review shows that computer vision enables the automation of object detection, classification, and elimination tasks in industrial processes, using solutions based on Python, OpenCV, and MATLAB. For this reason, the design of a computer vision recognition system with OpenCV is proposed, which allows automatic discarding of ceramics with defects using an algorithm for detecting ceramics with a camera and Arduino-based hardware, comparing the captured images with a standard image on a conveyor belt. The machine vision system was integrated with a camera connected to a computer running OpenCV, achieving effective automatic detection with a threshold of 25% difference from the standard part. This percentage was calculated by comparing the grayscale pixel values with a reference image. The system calculates the proportion of pixels that exceed the similarity threshold. The conclusion is that the developed system contributes to production, highlighting the possibility of future industrial integration.
Performance analysis of classification models to determine the health status of edge computing devices Ricardo Yauri; Nora Bertha La Serna Palomino
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11905

Abstract

Artificial intelligence (AI) has contributed to the development of autonomous systems in the healthcare field by integrating machine learning models, whose evaluation on resource-limited hardware devices is important to ensure their efficiency. This research evaluates the performance of classification models in edge computing (EC) systems, considering metrics such as accuracy, latency, memory consumption, and energy efficiency on low-power microcontrollers using TinyML techniques. The processes involved include the development, implementation, and testing of algorithms on embedded hardware using differentiated preprocessing techniques and the validation of hypotheses through statistical analysis. The results show that the decision tree (DT) model is more efficient in terms of prediction time and energy consumption, while random forests (RFs) stand out for their greater accuracy. Furthermore, memory analysis reveals that models based on fully connected neural networks are more efficient in terms of RAM usage. This provides guidelines for selecting algorithms in resource-constrained environments.
Co-Authors Fernando Alvarado Nora Bertha La Serna Palomino Rafael Espino